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UID:event-8908@www.amse-aixmarseille.fr
DTSTAMP:20260421T151422Z
CREATED:20260421T151422Z
LAST-MODIFIED:20260421T151422Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Anthony Strittmatter
DTSTART:20220329T120000Z
DTEND:20220329T133000Z
DESCRIPTION:Ineffective fundraising lowers the resources charities can use 
 to provide goods. We combine a field experiment and a causal machine-learni
 ng approach to increase a charity’s fundraising effectiveness. The approa
 ch optimally targets a fundraising instrument to individuals whose expected
  donations exceed solicitation costs. Our results demonstrate that machine-
 learning-based optimal targeting allows the charity to substantially increa
 se donations net of fundraising costs relative to uniform benchmarks in whi
 ch either everybody or no one receives the gift. To that end\, it (a) shoul
 d direct its fundraising efforts to a subset of past donors and (b) never a
 ddress individuals who were previously asked but never donated. Further\, w
 e show that the benefits of machine-learning-based optimal targeting even m
 aterialize when the charity only exploits publicly available geospatial inf
 ormation or applies the estimated optimal targeting rule to later fundraisi
 ng campaigns conducted in similar samples. We conclude that charities not e
 ngaging in optimal targeting waste significant resources.\\n\\nContact: Mic
 hel Lubrano : michel.lubrano[at]univ-amu.frPierre Michel : pierre.michel[a
 t]univ-amu.fr\n\nPlus d'informations: https://www.amse-aixmarseille.fr/fr/e
 venements/anthony-strittmatter
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://www.amse-aixmarseille.fr/fr/evenements/anthony-strittmatter
CONTACT:Michel Lubrano : michel.lubrano[at]univ-amu.frPierre Michel :&nbsp\
 ;pierre.michel[at]univ-amu.fr
TRANSP:OPAQUE
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